Mitigating the COVID-19 Pandemic through Data-Driven Resource Sharing

Posted: 2 Oct 2020 Last revised: 31 Jan 2022

See all articles by Esmaeil Keyvanshokooh

Esmaeil Keyvanshokooh

Department of Information & Operations Management, Mays Business School, Texas A&M University, College Station, TX

Mohammad Fattahi

Department of Technology and Innovation, University of Southern Denmark, Odense, Denmark

Pooyan Kazemian

Department of Operations, Weatherhead School of Management, Case Western Reserve University, Cleveland, OH, USA

Maryam Zokaeinikoo

Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA

Kenneth A Freedberg

Harvard Medical School

Date Written: October 1, 2020

Abstract

Outbreaks of COVID-19 in local communities can lead to a sharp increase in demand for limited resources such as mechanical ventilators. To cope with such demand surges, many hospitals in the US and other countries (1) purchased large quantities of mechanical ventilators, and (2) canceled or postponed elective procedures to preserve care capacity (including life-saving ventilators) for patients with COVID-19. These measures resulted in a substantial financial burden to the hospitals and poor outcomes for non-COVID-19 patients, a critical problem our resource sharing method can help mitigate. Given that COVID-19 spreads at varying rates across different regions, there is an opportunity for sharing scarce portable resources to alleviate capacity shortfalls caused by local outbreaks with fewer total resources.

This paper develops a novel data-driven adaptive robust simulation-based optimization (DARSO) methodology for optimal allocation and relocation of mechanical ventilators among different regions and states. Our method proposes an effective affine policy and considers a new type of dynamic uncertainty sets for ventilator demand by leveraging a powerful microsimulation model of COVID-19 spread and intervention. Our main methodological contributions lie in a new policy-guided model and an efficient algorithmic framework that mitigate critical limitations of current robust and stochastic models and make resource-sharing decisions implementable in real-time. Proof of concept is given for sharing ventilators among regions in Ohio and Michigan. Our numerical results suggest that the optimal policy derived from our data-driven adaptive robust model could satisfy ventilator demand during the first pandemic’s peak in Ohio and Michigan with 14% (limited sharing) to 63\% (full sharing) fewer ventilators compared to a no sharing strategy (status quo), thereby allowing hospitals to preserve more elective procedures. Furthermore, we demonstrate that sharing unused ventilators (rather than purchasing new machines) can result in 5% (limited sharing) to 44% (full sharing) lower annual cost, compared to no sharing, considering the transshipment and new ventilator costs. Our DARSO method is flexible and can be readily adopted to inform optimal sharing of other portable resources such as healthcare personnel, personal protective equipment (PPE), and point-of-care testing units.

Keywords: Data-driven optimization, Simulation, COVID-19

Suggested Citation

Keyvanshokooh, Esmaeil and Fattahi, Mohammad and Kazemian, Pooyan and Zokaeinikoo, Maryam and Freedberg, Kenneth A, Mitigating the COVID-19 Pandemic through Data-Driven Resource Sharing (October 1, 2020). Available at SSRN: https://ssrn.com/abstract=3703181

Esmaeil Keyvanshokooh (Contact Author)

Department of Information & Operations Management, Mays Business School, Texas A&M University, College Station, TX ( email )

210 Olsen Blvd
College Station, TX 77843
United States

Mohammad Fattahi

Department of Technology and Innovation, University of Southern Denmark, Odense, Denmark ( email )

Pooyan Kazemian

Department of Operations, Weatherhead School of Management, Case Western Reserve University, Cleveland, OH, USA ( email )

United States

Maryam Zokaeinikoo

Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA ( email )

Kenneth A Freedberg

Harvard Medical School ( email )

25 Shattuck St
Boston, MA 02115
United States

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